A joint evaluation method of regulated‐learning and cognitive quality in collaborative knowledge building.

Background: Both socially regulated learning and cognitive quality are important factors affecting collaborative knowledge building, but the current research lacks a joint quantified evaluation method that combines these two aspects. Objectives: Based on the existing framework, we proposed a joint evaluation method for regulated learning and cognitive quality in collaborative knowledge building, and further extracted the evaluation indicators. Methods: This study involved 74 learners enrolled in a course named 'the Application of Modern Educational Technology' as the subject of study, and made a case analysis with this method. Results and Conclusions: First, the indicators of this joint evaluation method could complement the indicators of traditional frequency statistics methods and facilitate an analysis of regulated learning and cognitive quality in collaborative knowledge building. Second, the group with a higher proportion of high‐level cognitive quality and socially shared regulation in collaborative knowledge building achieved better final scores. Finally, four types of groups were obtained through clustering: excellent, risky, mixed group, and highly collaborative group. This study introduced a joint evaluation method for regulated learning and cognitive quality in collaborative knowledge building and provides suggestions for future research. Lay Description: What is currently known about this topic?: Online collaborative knowledge building is increasingly important.Regulated learning and cognitive quality are considered very important for successful collaborative learning.Without quantitative methods, it is difficult to assess regulated learning and cognitive quality during collaborative knowledge building in a timely manner. What does this paper add?: This paper provided a joint evaluation method of regulated learning and cognitive quality (JERC) for online collaborative knowledge building.A case study was conducted to identify JERC's usefulness. Using descriptive statistics and visualization, it was determined that indicators of JERC can not only capture the state of cognitive quality and regulated learning status, but also elucidate the nature of that status (i.e., comprehensive, dynamic, cyclical).Through clustering analysis, four distinct groups were identified, each exhibiting different characteristics and moving patterns of regulated learning and cognitive quality over time. These patterns indicate that the quality of cognitive and regulatory learning cannot be determined solely by behavioural frequency. Moreover, an excessive proportion of individual regulation cannot predict better learning performance. Implications for practice: A computable representation model of regulated learning and cognitive quality was constructed to support automated perception and intervention in regulated learning and cognitive processing.With JERC, it is possible for researchers to capture, process, analyse and predict dynamic data to examine online collaborative knowledge building combined with shared physiological arousal events.The results of JERC can be used by researchers to develop advanced techniques, such as AI deep learning, for designing adaptive supports that can foster students' online collaborative knowledge building. [ABSTRACT FROM AUTHOR]